datafev.routines.charging_control package

Submodules

datafev.routines.charging_control.centralized_milp module

src.datafev.routines.charging_control.centralized_milp.charging_routine(ts, t_delta, horizon, system, solver, penalty_parameters)

This routine is executed periodically during operation of charger clusters.

It addresses the scenarios where EVs connected in clusters have previously defined charging schedules that may require deviations due to the local power consumption constraints of clusters. The control architecture is centralized; therefore, all clusters are controlled by a single decision-maker. The applied control is based on MILP rescheduling.

Parameters:

• ts (datetime) – Current time.

• t_delta (timedelta) – Control horizon.

• horizon (timedelta) – Optimization horizon of rescheduling.

• system (data_handling.multi_cluster) – Multi-cluster system object.

• solver (pyomo SolverFactory object) – Optimization solver.

• penalty_parameters (dict) – Cost parameters for capacity violation/devations.

Return type:

None.

datafev.routines.charging_control.decentralized_fcfs module

src.datafev.routines.charging_control.decentralized_fcfs.charging_routine(ts, t_delta, system)

This routine is executed periodically during operation of charger clusters.

It addresses the scenarios where each cluster has a local power consumption constraint and therefore has to control the power distribution to the chargers. The control architecture is decentralized; therefore, each cluster applies its own control. The applied control is based on “first-come-first-serve” logic.

Parameters:

• ts (datetime) – Current time.

• t_delta (timedelta) – Control horizon.

• system (data_handling.multi_cluster) – Multi-cluster system object.

Return type:

None.

datafev.protocols.charging_control.decentralized_llf module

src.datafev.routines.charging_control.decentralized_llf.charging_routine(ts, t_delta, system)

This routine is executed periodically during operation of charger clusters.

It addresses the scenarios where each cluster has a local power consumption constraint and therefore has to control the power distribution to the chargers. The control architecture is decentralized; therefore, each cluster applies its own control. The applied control is based on “least-laxity-first” logic.

Parameters:

• ts (datetime) – Current time.

• t_delta (timedelta) – Control horizon.

• system (data_handling.multi_cluster) – Multi-cluster system object.

Return type:

None.

datafev.routines.charging_control.decentralized_milp module

src.datafev.routines.charging_control.decentralized_milp.charging_routine(ts, t_delta, horizon, system, solver, penalty_parameters)

This routine is executed periodically during operation of charger clusters.

It addresses the scenarios where EVs connected in clusters have previously defined charging schedules that may require deviations due to the local power consumption constraints of clusters. The control architecture is decentralized; therefore, each cluster applies its own control. The applied control is based on MILP rescheduling.

Parameters:

• ts (datetime) – Current time.

• t_delta (timedelta) – Control horizon.

• horizon (timedelta) – Optimization horizon of rescheduling.

• system (data_handling.multi_cluster) – Multi-cluster system object.

• solver (pyomo SolverFactory object) – Optimization solver.

• penalty_parameters (dict) – Cost parameters for capacity violation / devations.

Return type:

None.